polars.scan_ipc#
- polars.scan_ipc(
- source: str | Path | list[str] | list[Path],
- *,
- n_rows: int | None = None,
- cache: bool = True,
- rechunk: bool = False,
- row_index_name: str | None = None,
- row_index_offset: int = 0,
- storage_options: dict[str, Any] | None = None,
- memory_map: bool = True,
- retries: int = 0,
Lazily read from an Arrow IPC (Feather v2) file or multiple files via glob patterns.
This allows the query optimizer to push down predicates and projections to the scan level, thereby potentially reducing memory overhead.
- Parameters:
- source
Path to a IPC file.
- n_rows
Stop reading from IPC file after reading
n_rows.- cache
Cache the result after reading.
- rechunk
Reallocate to contiguous memory when all chunks/ files are parsed.
- row_index_name
If not None, this will insert a row index column with give name into the DataFrame
- row_index_offset
Offset to start the row index column (only use if the name is set)
- storage_options
Extra options that make sense for
fsspec.open()or a particular storage connection. e.g. host, port, username, password, etc.- memory_map
Try to memory map the file. This can greatly improve performance on repeated queries as the OS may cache pages. Only uncompressed IPC files can be memory mapped.
- retries
Number of retries if accessing a cloud instance fails.